class_docstr: The collator is a standard huggingface collate.
  No need to change anything here.
code: def __call__(self, features, return_tensors=None):
      if return_tensors is None:
          return_tensors = self.return_tensors
      labels = (
          [feature["labels"] for feature in features]
          if "labels" in features[0].keys()
          else None
      )
      # We have to pad the labels before calling `tokenizer.pad` as this
      # method won't pad them and needs them of the
      # same length to return tensors.
      if labels is not None:
          max_label_length = max(len(lab) for lab in labels)
          if self.pad_to_multiple_of is not None:
              max_label_length = (
                  (max_label_length + self.pad_to_multiple_of - 1)
                  // self.pad_to_multiple_of
                  * self.pad_to_multiple_of
              )
  
          padding_side = self.tokenizer.padding_side
          for feature in features:
              remainder = [self.label_pad_token_id] * (
                  max_label_length - len(feature["labels"])
              )
              if isinstance(feature["labels"], list):
                  feature["labels"] = (
                      feature["labels"] + remainder
                      if padding_side == "right"
                      else remainder + feature["labels"]
                  )
              elif padding_side == "right":
                  feature["labels"] = np.concatenate(
                      [feature["labels"], remainder]
                  ).astype(np.int64)
              else:
                  feature["labels"] = np.concatenate(
                      [remainder, feature["labels"]]
                  ).astype(np.int64)
  
      features = self.tokenizer.pad(
          features,
          padding=self.padding,
          max_length=self.max_length,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors=return_tensors,
      )
  
      # prepare decoder_input_ids
      if (
          labels is not None
          and self.model is not None
          and hasattr(
              self.model, "prepare_decoder_input_ids_from_labels"
          )
      ):
          decoder_input_ids = (
              self.model.prepare_decoder_input_ids_from_labels(
                  labels=features["labels"]
              )
          )
          features["decoder_input_ids"] = decoder_input_ids
  
      return features
  
imports: from avalanche.benchmarks.utils import DataAttribute, ConstantSequence
  from avalanche.training.plugins import ReplayPlugin
  from dataclasses import dataclass
  from transformers import PreTrainedTokenizerBase
  from typing import Optional, Union, Any
  from transformers.utils import PaddingStrategy
  import torch
  import avalanche
  import torch.nn
  from avalanche.benchmarks import CLScenario, CLStream, CLExperience
  from avalanche.evaluation.metrics import accuracy_metrics
  import avalanche.training.templates.base
  from avalanche.benchmarks.utils import AvalancheDataset
  from transformers import AutoTokenizer
  from transformers import T5ForConditionalGeneration
  from datasets import load_dataset
  import numpy
label_desc: Use image node_id 1 for calling the CustomDataCollatorSeq2SeqBeta obj's underlying member method code with example usage: obj.__call__(features, return_tensors) and returns: features